Contents

1 Description

TSIS is an R package for detecting transcript isoform switch for time-series data. Transcript isoform switch occurs when a pair of isoforms reverse the order of expression levels as shown in Figure 1. TSIS characterizes the transcript switch by 1) defining the isoform switch time points for any pair of transcript isoforms within a gene, 2) describing the switch using 5 different features, 3) filtering the results with user’s specifications and 4) visualizing the results using different plots for the user to examine further details of the switches. All the functions are available in the forms of a graphic interface implemented by Shiny App (a web application framework for R) (Chang, et al., 2016), in which users can implement the analysis as easy as mouse click. The tool can also be run just in command lines without graphic interface. This tutorial will cover both in the following sections.

Figure 1: Isoform switch analysis methods. Expression data with 3 replicates for each condition/time point is simulated for isoforms \(iso_i\) and \(iso_j\). The points in the plots represent the samples and the black lines are the average of samples. (A) is the iso-kTSP algorithm for comparisons of two conditions \(c_1\) and \(c_2\). The iso-kTSP is extended to time-series isoform switch (TSIS) in figure (B). The time-series with 6 time points is divided into 4 intervals by the intersection points of average expression. Five features for switch evaluation are determined based on the intervals before and after switch, e.g. the before and after intervals adjoined to switch point \(P_i\).

1.1 Determine switch points

Given that a pair of isoforms \(iso_i\) and \(iso_j\) may have a number of switches in a time-series, we have offered two approaches to search in TSIS:

1.2 Define switch scoring features

After switch points are determined, we define each switch by 1) the switch point \(P_i\) , 2) time points between switch points \(P_{i-1}\) and \(P_i\) as interval before switch \(P_i\) and 3) time points between switch points \(P_i\) and \(P_{i+1}\) as interval after the switch \(P_i\) (see Figure 1(B)). We defined 5 features to score each isoform switch. The first two are the probability/frequency of switch and the sum of average distance before and after switch, used as Score 1 and Score 2 in iso-kTSP method (Sebestyen, et al., 2015) (see Figure 1(A))).

2 Installation and loading

2.1 Check before installation

Due to an issue with devtools, if R software is installed in a directory whose name has space character in it, e.g. in “C:\Program Files”, users may get error message “‘C:\Program’ is not recognized as an internal or external command”. This issue has to be solved by making sure that R is installed in a directory whose name has no space characters. Users can check the R installation location by typing

R.home()

2.2 Install dependency packages

install.packages(c(“shiny”, “shinythemes”,“ggplot2”,“plotly”,“zoo”,“gtools”,“devtools”), dependencies=TRUE)

2.3 Install TSIS package

Install iso-kTSP package from Github using devtools package.

library(devtools)
devtools::install_github("wyguo/TSIS")

2.4 Loading

Once installed, TSIS package can be loaded as normal

library(TSIS)

3 Shiny App – as easy as mouse click

To make the implement more user friendly, TSIS analysis is integrated into a Shiny App (a web application framework for R) (Chang, et al., 2016). By typing

TSIS.app()

in R console after loading TSIS package, the App is opened in the default web browser. Users can upload input datasets, set parameters for switch analysis, visualize and save the results as easy as mouse click. The Shiny App includes three tab panels (see Figure 2).

Figure 2: The tab panels in Shiny App.

3.1 Tab panel 1: Manual

The first tab panel includes this user manual.

3.2 Tab panel 2: Isoform switch analysis

There are four sections in this panel.

3.2.1 Input data files

Three types of information are required for TSIS analysis.

  • Information 1: Time-series isoform expression data with each row represents an isoform and each column represents a sample (sample replicates followed by one column to another).
  • Information 2: Gene and isoform mapping table corresponding to Dataset 1, with first column of gene names and second column of isoform names.
  • Information 3: Optional. Names of subset of isoforms. Users can output subset of results by providing a list of isoforms, for example the protein coding transcripts.

Figure 3(A) shows the data input interface for time-series isoform expression and gene-isoform mapping. By clicking the “Browse…” button, a window is open for data loading (Figure 3(B)). Users can use the interface shown in Figure 3(C) to load the names of subset of isoforms. Please see the following data examples for data format details.


Figure 3: Interface for input information.

The TSIS package provides the example datasets “AtRTD2” with 2,666 genes and 6,307 isoforms, analysed in 26 time points, each with 3 biological replicates and 3 technical replicates. The experiments were designed to investigate the Arabidopsis gene expression response to cold. The isoform expression is in TPM (transcript per million) format. For the experiments and data quantification details, please see the AtRTD2 paper (Zhang, et al.,2016). Typing the following command to see data information.

##26 time points, 3 biological replicates and 3 technical replicates, in total 234 sample points. 
library(TSIS)
AtRTD2$data.exp[1:10,1:3]
AtRTD2$mapping[1:10,]
AtRTD2$sub.isoforms[1:10]

Note: The data loaded into the Shiny App must be in *.csv format for loading convenience. Users can download the example datasets from https://github.com/wyguo/TSIS/tree/master/data or by typing the following codes:

AtRTD2.example()

The data will be saved in a folder “example data” in the working directory. Figure 4 shows the examples of input data in csv format.

Figure 4: The format of input csv files for (A) transcript isoform expression, (B) two column table of gene-isoform mapping and (C) one column of a subset of isoform names.

3.2.2 Parameter settings

3.2.2.1 Scoring parameters

This section is used to set the parameters for TSIS scoring features. The parameters can be set by selecting or typing in corresponding boxes. Scoring process is starting by clicking the “Scoring” button. The parameter setting details are in the text followed the scoring button. Processing tacking bars for time-series intersection points searching (Figure 5(B)) and switch scoring (Figure 5(C)) for the isoform pairs will present in the bottom of the browser.


Figure 5: Scoring parameter input interface and the processing tracking bars.

3.2.2.2 Filtering parameters

Figure 6 is the interface for scoring feature filtering. Users can set cut-offs, such as for the probability/frequency of switch and sum of average distances, to further refine the switch results. The parameter setting details are in the text under the “Filtering” button.


Figure 6: The interface for switch feature filtering.

3.3 Density of switch points

The isoform switches occur at different time points in the time-series. To visualize the frequency and density plot of switch time, TSIS Shiny App provides the plot interface as shown in Figure 7. Frequency and density bar plots and line plots, which correspond to the “x.value” of switch time column in the output table in Figure 8, will present by clicking the corresponding radio buttons. The plot can be saved in html, pdf and png format.

Note: The plot is made by using plotly R package. Users can move the mouse around the plot to show plot values and select part of the plot to zoom out. More actions are available by using the tool bar in the top right corner of the plot.

Figure 7: Switch time density and frequency plot interface.

3.4 Output scores of isoform switch

The output table of switch features after scoring or filtering. The columns include the information of isoform names, isoform ratios to genes, the intervals before and after switch, the coordinates of switch points and five features of switch quality. Table columns can be sorted by clicking the small triangles beside the column names and contents can be searched by typing text in the search box. The explanations for each column are on the top of the table (see Figure 8).

Figure 8: The output score feature table.

4 Tab panel 3: Switch visualization

4.1 Switch plots

This part is used to make a time-series plot of a pair of isoforms by providing their names. Plot type options are error bar plot and ribbon plot (see functions geom_errorbar and geom_smooth in ggplot2 package for details) as shown in Figure 9 and example plots of AT5G60930 in Figure 11 and Figure 12. An option is provided to only label the features of switch points with probability/frequency of switch>cut-off in the time region for investigation. The plots can be saved in html (plotly format plot), png or pdf format.

Figure 9: Isoform switch plot interface. (A) is the error bar type plot while (B) is the ribbon type plot.

4.2 Multiple plots for switch

This section is used to save top n (ranking with Feature 1 probability/frequency of switch) pairs of isoforms into png or pdf format plots (see Figure 10).

Figure 9: Multiple isoform switch plot interface.

5 TSIS scripts – step by step analysis

In addition to the Shiny App, users can use scripts to do TSIS analysis in R console. The following examples show a step-by-step tutorial of the analysis. Please refer to the function details using help function, e.g. help(iso.switch) or ?iso.switch.

5.1 Loading datasets

##load the data
library(TSIS)
data.exp<-AtRTD2$data.exp
mapping<-AtRTD2$mapping
dim(data.exp);dim(mapping)

5.2 Scoring

Example 1: search intersection points with mean expression

##Scores
scores.mean2int<-iso.switch(data.exp=data.exp,mapping =mapping,
                     t.start=1,t.end=26,nrep=9,rank=F,
                     min.t.points =2,min.distance=1,spline =F,spline.df = 9,verbose = F)


Example 2: search intersection points with spline method

##Scores, set spline=T and define spline degree of freedom to spline.df=9
scores.spline2int<-iso.switch(data.exp=data.exp,mapping =mapping,
                     t.start=1,t.end=26,nrep=9,rank=F,
                     min.t.points =2,min.distance=1,spline =T,spline.df = 9,verbose = F)

5.3 Filtering

Example 1, general filtering with cut-offs

##intersection from mean expression
scores.mean2int.filtered<-score.filter(scores = scores.mean2int,prob.cutoff = 0.5,dist.cutoff = 1,
                                       t.points.cutoff = 2,pval.cutoff = 0.01, cor.cutoff = 0.5,
                                       data.exp = NULL,mapping = NULL,sub.isoform.list = NULL,
                                       sub.isoform = F,max.ratio = F,x.value.limit = c(9,17) )

scores.mean2int.filtered[1:5,]
##intersection from spline method
scores.spline2int.filtered<-score.filter(scores = scores.spline2int,prob.cutoff = 0.5,
                                         dist.cutoff = 1,t.points.cutoff = 2,pval.cutoff = 0.01,
                                         cor.cutoff = 0.5,data.exp = NULL,mapping = NULL,
                                         sub.isoform.list = NULL,sub.isoform = F,max.ratio = F,
                                         x.value.limit = c(9,17) )


Example 2, only show subset of results according to an isoform list

##intersection from mean expression
##input a list of isoform names for investigation.
sub.isoform.list<-AtRTD2$sub.isoforms
sub.isoform.list[1:10]
##assign the isoform name list to sub.isoform.list and set sub.isoform=TRUE
scores.mean2int.filtered.subset<-score.filter(scores = scores.mean2int,prob.cutoff = 0.5,dist.cutoff = 1,
                                       t.points.cutoff = 2,pval.cutoff = 0.01, cor.cutoff = 0.5,
                                       data.exp = NULL,mapping = NULL,sub.isoform.list = sub.isoform.list,
                                       sub.isoform = T,max.ratio = F,x.value.limit = c(9,17) )


Example 3, only show results of isoforms of maximum ratios to genes

scores.mean2int.filtered.maxratio<-score.filter(scores = scores.mean2int,prob.cutoff = 0.5,dist.cutoff = 1,
                                       t.points.cutoff = 2,pval.cutoff = 0.01, cor.cutoff = 0,
                                       data.exp = data.exp,mapping = mapping,sub.isoform.list = NULL,
                                       sub.isoform = F,max.ratio = T,x.value.limit = c(9,17) )

5.4 Make plots

5.4.1 Error bar plot

plotTSIS(data2plot = data.exp,scores = scores.mean2int.filtered,iso1 = 'AT5G60930_P2',
        iso2 = ' AT5G60930_P3',gene.name = NULL,y.lab = 'Expression',make.plotly = F,
        t.start = 1,t.end = 26,nrep = 9,prob.cutoff = 0.5,x.lower.boundary = 9,
        x.upper.boundary = 17,show.region = T,show.scores = T,
        line.width =0.5,point.size = 3,error.type = 'stderr',show.errorbar = T,errorbar.size = 0.5,
        errorbar.width = 0.2,spline = F,spline.df = NULL,ribbon.plot = F )

5.4.2 Ribbon plot

plotTSIS(data2plot = data.exp,scores = scores.mean2int.filtered,iso1 = 'AT5G60930_P2',
        iso2 = 'AT5G60930_P3',gene.name = NULL,y.lab = 'Expression',make.plotly = F,
        t.start = 1,t.end = 26,nrep = 9,prob.cutoff = 0.5,x.lower.boundary = 9,
        x.upper.boundary = 17,show.region = T,show.scores = T,error.type = 'stderr',
        line.width =0.5,point.size = 3,show.errorbar = T,errorbar.size = 0.5,
        errorbar.width = 0.2,spline = F,spline.df = NULL,ribbon.plot = T )

6 References

Chang, W., et al. 2016. shiny: Web Application Framework for R. https://CRAN.R-project.org/package=shiny

Sebestyen, E., Zawisza, M. and Eyras, E. Detection of recurrent alternative splicing switches in tumor samples reveals novel signatures of cancer. Nucleic Acids Res 2015;43(3):1345-1356.

Zhang, R., et al. AtRTD2: A Reference Transcript Dataset for accurate quantification of alternative splicing and expression changes in Arabidopsis thaliana RNA-seq data. bioRxiv 2016.

7 Session Information

## R version 3.3.1 (2016-06-21)
## Platform: x86_64-w64-mingw32/x64 (64-bit)
## Running under: Windows 7 x64 (build 7601) Service Pack 1
## 
## locale:
## [1] LC_COLLATE=English_United Kingdom.1252 
## [2] LC_CTYPE=English_United Kingdom.1252   
## [3] LC_MONETARY=English_United Kingdom.1252
## [4] LC_NUMERIC=C                           
## [5] LC_TIME=English_United Kingdom.1252    
## 
## attached base packages:
## [1] stats     graphics  grDevices utils     datasets  methods   base     
## 
## other attached packages:
## [1] BiocStyle_2.3.30  devtools_1.12.0   plotly_4.5.2      ggplot2_2.1.0    
## [5] shinythemes_1.1.1 shiny_0.14.2      TSIS_0.1.0       
## 
## loaded via a namespace (and not attached):
##  [1] Rcpp_0.12.9       plyr_1.8.4        base64enc_0.1-3  
##  [4] tools_3.3.1       digest_0.6.10     jsonlite_1.0     
##  [7] memoise_1.0.0     evaluate_0.10     tibble_1.1       
## [10] gtable_0.2.0      viridisLite_0.1.3 DBI_0.5          
## [13] yaml_2.1.14       withr_1.0.2       dplyr_0.5.0      
## [16] httr_1.2.1        stringr_1.0.0     knitr_1.15.1     
## [19] htmlwidgets_0.8   rprojroot_1.2     grid_3.3.1       
## [22] R6_2.1.2          rmarkdown_1.3     purrr_0.2.2      
## [25] tidyr_0.6.0       magrittr_1.5      codetools_0.2-14 
## [28] scales_0.4.0      backports_1.0.5   htmltools_0.3.5  
## [31] assertthat_0.1    mime_0.5          xtable_1.8-2     
## [34] colorspace_1.2-6  httpuv_1.3.3      stringi_1.1.1    
## [37] lazyeval_0.2.0    munsell_0.4.3